Research on predicting network traffic using neural networks

• Main Authors: WANG, Zhaoxia, SUN, Yugeng, WANG, Zhiyong, Hao, T., Sun, X., Qin, J., SHEN, Huayu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: Back-propagation (BP) algorithms; Davidon least squares-based learning algorithm; Network traffic predicting; Neural network (NN); Numerical Analysis and Scientific Computing; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/5638
• Institution: Singapore Management University

This paper used Back-propagation (BP) algorithms and Davidon least squares-based learning algorithm to train the neural network (NN) to predict the nonlinear self-similar network traffic respectively. The feasibility and advantage of these two algorithms were discussed by analyzing the Mean learning errors, training errors and the convergent speed of these two training algorithms. The simulation demonstrated that the NN trained by both of these two training algorithms can well predict this traffic. Compared with BP algorithms, the Davidon least squares-based learning algorithm can converge quickly and has the almost same prediction accuracy. It supplied a feasible method to predict the complex self-similar network traffic.